OpenRaiser/Intern-Atlas huggingface dataset:
Concepts for designing modern C++ interfaces for MPI
Venue: arXiv cs.DC 2025
Year: 2025

Since the C++ bindings were deleted in 2008, the Message Passing Interface (MPI) community has revived efforts in building high-level modern C++ interfaces. Such interfaces are either built to serve specific scientific application needs (with limited coverage to the underlying MPI functionalities), or as an exercise in general-purpose programming model building, with the hope that bespoke interfaces can be broadly adopted to construct a variety of distributed-memory scientific applications. However, with the advent of modern C++-based heterogeneous programming models, GPUs and widespread Machine Learning (ML) usage in contemporary scientific computing, the role of prospective community-standardized high-level C++ interfaces to MPI is evolving. The success of such an interface clearly will depend on providing robust abstractions and features adhering to the generic programming principles that underpin the C++ programming language, without compromising on either performance and portability, the core principles upon which MPI was founded. However, there is a tension between idiomatic C++ handling of types and lifetimes and MPI's loose interpretation of object lifetimes/ownership and insistence on maintaining global states. Instead of proposing "yet another" high-level C++ interface to MPI, overlooking or providing partial solutions to work around the key issues concerning the dissonance between MPI semantics and idiomatic C++, this paper focuses on the three fundamental aspects of a high-level interface: type system, object lifetimes and communication buffers, also identifying inconsistencies in the MPI specification. Presumptive solutions can be unrefined, and we hope the broader MPI and C++ communities will engage with us in productive exchange of ideas and concerns.
AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation
Venue: arXiv cs.CL 2025
Year: 2025

The recent development and wider accessibility of LLMs have spurred discussions about how they can be used in survey research, including classifying open-ended survey responses. Due to their linguistic capacities, it is possible that LLMs are an efficient alternative to time-consuming manual coding and the pre-training of supervised machine learning models. As most existing research on this topic has focused on English-language responses relating to non-complex topics or on single LLMs, it is unclear whether its findings generalize and how the quality of these classifications compares to established methods. In this study, we investigate to what extent different LLMs can be used to code open-ended survey responses in other contexts, using German data on reasons for survey participation as an example. We compare several state-of-the-art LLMs and several prompting approaches, and evaluate the LLMs' performance by using human expert codings. Overall performance differs greatly between LLMs, and only a fine-tuned LLM achieves satisfactory levels of predictive performance. Performance differences between prompting approaches are conditional on the LLM used. Finally, LLMs' unequal classification performance across different categories of reasons for survey participation results in different categorical distributions when not using fine-tuning. We discuss the implications of these findings, both for methodological research on coding open-ended responses and for their substantive analysis, and for practitioners processing or substantively analyzing such data. Finally, we highlight the many trade-offs researchers need to consider when choosing automated methods for open-ended response classification in the age of LLMs. In doing so, our study contributes to the growing body of research about the conditions under which LLMs can be efficiently, accurately, and reliably leveraged in survey research.
HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search
Venue: arXiv cs.DB 2025
Year: 2025

Approximate Nearest Neighbor Search (ANNS) is essential for various data-intensive applications, including recommendation systems, image retrieval, and machine learning. Scaling ANNS to handle billions of high-dimensional vectors on a single machine presents significant challenges in memory capacity and processing efficiency. To address these challenges, distributed vector databases leverage multiple nodes for the parallel storage and processing of vectors. However, existing solutions often suffer from load imbalance and high communication overhead, primarily due to traditional partition strategies that fail to effectively distribute the workload. In this paper, we introduce Harmony, a distributed ANNS system that employs a novel multi-granularity partition strategy, combining dimension-based and vector-based partition. This strategy ensures a balanced distribution of computational load across all nodes while effectively minimizing communication costs. Furthermore, Harmony incorporates an early-stop pruning mechanism that leverages the monotonicity of distance computations in dimension-based partition, resulting in significant reductions in both computational and communication overhead. We conducted extensive experiments on diverse real-world datasets, demonstrating that Harmony outperforms leading distributed vector databases, achieving 4.63 times throughput on average in four nodes and 58% performance improvement over traditional distribution for skewed workloads.
How Warm-Glow Alters the Usability of Technology
Venue: arXiv cs.HC 2025
Year: 2025

As technology increasingly aligns with users' personal values, traditional models of usability, focused on functionality and specifically effectiveness, efficiency, and satisfaction, may not fully capture how people perceive and evaluate it. This study investigates how the warm-glow phenomenon, the positive feeling associated with doing good, shapes perceived usability. An experimental approach was taken in which participants evaluated a hypothetical technology under conditions designed to evoke either the intrinsic (i.e., personal fulfillment) or extrinsic (i.e., social recognition) dimensions of warm-glow. A Multivariate Analysis of Variance as well as subsequent follow-up analyses revealed that intrinsic warm-glow significantly enhances all dimensions of perceived usability, while extrinsic warm-glow selectively influences perceived effectiveness and satisfaction. These findings suggest that perceptions of usability extend beyond functionality and are shaped by how technology resonates with users' broader sense of purpose. We conclude by proposing that designers consider incorporating warm-glow into technology as a strategic design decision.
Generating uniform linear extensions using few random bits
Venue: arXiv cs.CC 2025
Year: 2025

A \emph{linear extension} of a partial order \(\preceq\) over items \(A = \{ 1, 2, \ldots, n \}\) is a permutation \(σ\) such that for all \(i < j\) in \(A\), it holds that \(\neg(σ(j) \preceq σ(i))\). Consider the problem of generating uniformly from the set of linear extensions of a partial order. The best method currently known uses \(O(n^3 \ln(n))\) operations and \(O(n^3 \ln(n)^2)\) iid fair random bits to generate such a permutation. This paper presents a method that generates a uniform linear extension using only \(2.75 n^3 \ln(n)\) operations and \( 1.83 n^3 \ln(n) \) iid fair bits on average.
Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

We present Sparsh-X, the first multisensory touch representations across four tactile modalities: image, audio, motion, and pressure. Trained on ~1M contact-rich interactions collected with the Digit 360 sensor, Sparsh-X captures complementary touch signals at diverse temporal and spatial scales. By leveraging self-supervised learning, Sparsh-X fuses these modalities into a unified representation that captures physical properties useful for robot manipulation tasks. We study how to effectively integrate real-world touch representations for both imitation learning and tactile adaptation of sim-trained policies, showing that Sparsh-X boosts policy success rates by 63% over an end-to-end model using tactile images and improves robustness by 90% in recovering object states from touch. Finally, we benchmark Sparsh-X ability to make inferences about physical properties, such as object-action identification, material-quantity estimation, and force estimation. Sparsh-X improves accuracy in characterizing physical properties by 48% compared to end-to-end approaches, demonstrating the advantages of multisensory pretraining for capturing features essential for dexterous manipulation.
Reasoning with Exploration: An Entropy Perspective
Venue: arXiv cs.CL 2025
Year: 2025

Balancing exploration and exploitation is a central goal in reinforcement learning (RL). Despite recent advances in enhancing large language model (LLM) reasoning, most methods lean toward exploitation, and increasingly encounter performance plateaus. In this work, we revisit entropy -- a signal of exploration in RL -- and examine its relationship to exploratory reasoning in LLMs. Through empirical analysis, we uncover positive correlations between high-entropy regions and three types of exploratory reasoning actions: (1) pivotal tokens that determine or connect logical steps, (2) reflective actions such as self-verification and correction, and (3) rare behaviors under-explored by the base LLMs. Motivated by this, we introduce a minimal modification to standard RL with only one line of code: augmenting the advantage function with an entropy-based term. Unlike traditional maximum-entropy methods which encourage exploration by promoting uncertainty, we encourage exploration by promoting longer and deeper reasoning chains. Notably, our method achieves significant gains on the Pass@K metric -- an upper-bound estimator of LLM reasoning capabilities -- even when evaluated with extremely large K values, pushing the boundaries of LLM reasoning.
CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion Policy (DP) enables robots to learn complex behaviors by imitating expert demonstrations through action diffusion. However, in practical applications, hardware limitations often degrade data quality, while real-time constraints restrict model inference to instantaneous state and scene observations. These limitations seriously reduce the efficacy of learning from expert demonstrations, resulting in failures in object localization, grasp planning, and long-horizon task execution. To address these challenges, we propose Causal Diffusion Policy (CDP), a novel transformer-based diffusion model that enhances action prediction by conditioning on historical action sequences, thereby enabling more coherent and context-aware visuomotor policy learning. To further mitigate the computational cost associated with autoregressive inference, a caching mechanism is also introduced to store attention key-value pairs from previous timesteps, substantially reducing redundant computations during execution. Extensive experiments in both simulated and real-world environments, spanning diverse 2D and 3D manipulation tasks, demonstrate that CDP uniquely leverages historical action sequences to achieve significantly higher accuracy than existing methods. Moreover, even when faced with degraded input observation quality, CDP maintains remarkable precision by reasoning through temporal continuity, which highlights its practical robustness for robotic control under realistic, imperfect conditions.
ETS: Open Vocabulary Electroencephalography-To-Text Decoding and Sentiment Classification
Venue: arXiv cs.LG 2025
Year: 2025

Decoding natural language from brain activity using non-invasive electroencephalography (EEG) remains a significant challenge in neuroscience and machine learning, particularly for open-vocabulary scenarios where traditional methods struggle with noise and variability. Previous studies have achieved high accuracy on small-closed vocabularies, but it still struggles on open vocabularies. In this study, we propose ETS, a framework that integrates EEG with synchronized eye-tracking data to address two critical tasks: (1) open-vocabulary text generation and (2) sentiment classification of perceived language. Our model achieves a superior performance on BLEU and Rouge score for EEG-To-Text decoding and up to 10% F1 score on EEG-based ternary sentiment classification, which significantly outperforms supervised baselines. Furthermore, we show that our proposed model can handle data from various subjects and sources, showing great potential for high performance open vocabulary eeg-to-text system.
Predicting Onflow Parameters Using Transfer Learning for Domain and Task Adaptation
Venue: arXiv cs.LG 2025
Year: 2025

Determining onflow parameters is crucial from the perspectives of wind tunnel testing and regular flight and wind turbine operations. These parameters have traditionally been predicted via direct measurements which might lead to challenges in case of sensor faults. Alternatively, a data-driven prediction model based on surface pressure data can be used to determine these parameters. It is essential that such predictors achieve close to real-time learning as dictated by practical applications such as monitoring wind tunnel operations or learning the variations in aerodynamic performance of aerospace and wind energy systems. To overcome the challenges caused by changes in the data distribution as well as in adapting to a new prediction task, we propose a transfer learning methodology to predict the onflow parameters, specifically angle of attack and onflow speed. It requires first training a convolutional neural network (ConvNet) model offline for the core prediction task, then freezing the weights of this model except the selected layers preceding the output node, and finally executing transfer learning by retraining these layers. A demonstration of this approach is provided using steady CFD analysis data for an airfoil for i) domain adaptation where transfer learning is performed with data from a target domain having different data distribution than the source domain and ii) task adaptation where the prediction task is changed. Further exploration on the influence of noisy data, performance on an extended domain, and trade studies varying sampling sizes and architectures are provided. Results successfully demonstrate the potential of the approach for adaptation to changing data distribution, domain extension, and task update while the application for noisy data is concluded to be not as effective.
Predicting Anthropometric Body Composition Variables Using 3D Optical Imaging and Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of anthropometric body composition variables, such as Appendicular Lean Mass (ALM), Body Fat Percentage (BFP), and Bone Mineral Density (BMD), is essential for early diagnosis of several chronic diseases. Currently, researchers rely on Dual-Energy X-ray Absorptiometry (DXA) scans to measure these metrics; however, DXA scans are costly and time-consuming. This work proposes an alternative to DXA scans by applying statistical and machine learning models on biomarkers (height, volume, left calf circumference, etc) obtained from 3D optical images. The dataset consists of 847 patients and was sourced from Pennington Biomedical Research Center. Extracting patients' data in healthcare faces many technical challenges and legal restrictions. However, most supervised machine learning algorithms are inherently data-intensive, requiring a large amount of training data. To overcome these limitations, we implemented a semi-supervised model, the $p$-Laplacian regression model. This paper is the first to demonstrate the application of a $p$-Laplacian model for regression. Our $p$-Laplacian model yielded errors of $\sim13\%$ for ALM, $\sim10\%$ for BMD, and $\sim20\%$ for BFP when the training data accounted for 10 percent of all data. Among the supervised algorithms we implemented, Support Vector Regression (SVR) performed the best for ALM and BMD, yielding errors of $\sim 8\%$ for both, while Least Squares SVR performed the best for BFP with $\sim 11\%$ error when trained on 80 percent of the data. Our findings position the $p$-Laplacian model as a promising tool for healthcare applications, particularly in a data-constrained environment.
ViLLa: A Neuro-Symbolic approach for Animal Monitoring
Venue: arXiv cs.CV 2025
Year: 2025

Monitoring animal populations in natural environments requires systems that can interpret both visual data and human language queries. This work introduces ViLLa (Vision-Language-Logic Approach), a neuro-symbolic framework designed for interpretable animal monitoring. ViLLa integrates three core components: a visual detection module for identifying animals and their spatial locations in images, a language parser for understanding natural language queries, and a symbolic reasoning layer that applies logic-based inference to answer those queries. Given an image and a question such as "How many dogs are in the scene?" or "Where is the buffalo?", the system grounds visual detections into symbolic facts and uses predefined rules to compute accurate answers related to count, presence, and location. Unlike end-to-end black-box models, ViLLa separates perception, understanding, and reasoning, offering modularity and transparency. The system was evaluated on a range of animal imagery tasks and demonstrates the ability to bridge visual content with structured, human-interpretable queries.
Generalized Reference Kernel With Negative Samples For Support Vector One-class Classification
Venue: arXiv cs.LG 2025
Year: 2025

This paper focuses on small-scale one-class classification with some negative samples available. We propose Generalized Reference Kernel with Negative Samples (GRKneg) for One-class Support Vector Machine (OC-SVM). We study different ways to select/generate the reference vectors and recommend an approach for the problem at hand. It is worth noting that the proposed method does not use any labels in the model optimization but uses the original OC-SVM implementation. Only the kernel used in the process is improved using the negative data. We compare our method with the standard OC-SVM and with the binary Support Vector Machine (SVM) using different amounts of negative samples. Our approach consistently outperforms the standard OC-SVM using Radial Basis Function kernel. When there are plenty of negative samples, the binary SVM outperforms the one-class approaches as expected, but we show that for the lowest numbers of negative samples the proposed approach clearly outperforms the binary SVM.
FORTRESS: Frontier Risk Evaluation for National Security and Public Safety
Venue: arXiv cs.CY 2025
Year: 2025

The rapid advancement of large language models (LLMs) introduces dual-use capabilities that could both threaten and bolster national security and public safety (NSPS). Models implement safeguards to protect against potential misuse relevant to NSPS and allow for benign users to receive helpful information. However, current benchmarks often fail to test safeguard robustness to potential NSPS risks in an objective, robust way. We introduce FORTRESS: 500 expert-crafted adversarial prompts with instance-based rubrics of 4-7 binary questions for automated evaluation across 3 domains (unclassified information only): Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE), Political Violence & Terrorism, and Criminal & Financial Illicit Activities, with 10 total subcategories across these domains. Each prompt-rubric pair has a corresponding benign version to test for model over-refusals. This evaluation of frontier LLMs' safeguard robustness reveals varying trade-offs between potential risks and model usefulness: Claude-3.5-Sonnet demonstrates a low average risk score (ARS) (14.09 out of 100) but the highest over-refusal score (ORS) (21.8 out of 100), while Gemini 2.5 Pro shows low over-refusal (1.4) but a high average potential risk (66.29). Deepseek-R1 has the highest ARS at 78.05, but the lowest ORS at only 0.06. Models such as o1 display a more even trade-off between potential risks and over-refusals (with an ARS of 21.69 and ORS of 5.2). To provide policymakers and researchers with a clear understanding of models' potential risks, we publicly release FORTRESS at https://huggingface.co/datasets/ScaleAI/fortress_public. We also maintain a private set for evaluation.
Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders
Venue: arXiv cs.LG 2025
Year: 2025

Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders (AE) show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction error, the definition of this threshold directly impacts the performance of the detection process. Thus, this work proposes the automatic definition of this threshold using some machine learning algorithms. For this, three algorithms were evaluated: the K-Nearst Neighbors, the K-Means and the Support Vector Machine.
Plaintext-Scale Fair Data Exchange
Venue: arXiv cs.CR 2025
Year: 2025

The Fair Data Exchange (FDE) protocol (CCS'24) achieves atomic, pay-per-file exchange with a constant on-chain footprint, but existing implementations do not scale: proof verification can take hours even for files of only tens of megabytes. In this work, we present two FDE implementations: VECKplus and VECKstar. VECKplus reduces client-side verification to O(lambda) -- independent of file size -- where lambda is the security parameter. VECKplus brings verification time to approximately 1 s on a commodity desktop for any file size. VECKplus also significantly reduces proof generation time by limiting expensive range proofs to a Theta(lambda)-sized subset of the file. This improvement is especially beneficial for large files, even though proof generation and encryption are already precomputable and highly parallelizable on the server: for a 32 MiB file, for instance, proof generation time drops from approximately 6,295 s to approximately 4.8 s (approximately 1,300x speed-up). As in the existing ElGamal implementation, however, VECKplus retains exponential ElGamal over the full file. Consequently, the client must perform ElGamal decryption and download ciphertexts that are at least 10x the plaintext size. We address both drawbacks in the second implementation, VECKstar: we replace bulk ElGamal encryption with a fast, hash-derived mask and confine public-key work to a Theta(lambda) sample tied together with a file-size-independent zk-SNARK, adding less than 0.1 s to verification in our prototype. Importantly, this also reduces the communication overhead from at least 10x to less than 50%. Together, these changes yield plaintext-scale performance. Finally, we bridge Bitcoin's secp256k1 and BLS12-381 with a file-size-independent zk-SNARK to run FDE fully off-chain over the Lightning Network, reducing fees from approximately USD 10 to less than USD 0.01 and payment latency to a few seconds.